Skip to content

Music Informatics Group

Georgia Institute of Technology

  • blog
  • publications
  • contact

Projects & Events

  • EAsT: Embeddings As Teachers for Music Classification
  • Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
  • Score-Informed Networks for Music Performance Assessment
  • Multi-Task Learning for Instrument Activation Aware Music Source Separation
  • AR-VAE: Attribute-based Regularization of VAE Latent Spaces
  • dMelodies: A Music Dataset for Disentanglement Learning
  • Learning to Traverse Latent Spaces for Musical Score Inpainting
  • An Attention Mechanism for Musical Instrument Recognition
  • Explicitly Conditioned Melody Generation
  • Music Informatics Group @ICML Machine Learning for Music Discovery Workshop

Xambo et al. – 2016 – Learning to code through MIR

Xambo et al. - 2016 - Learning to code through MIR

Posted on August 16, 2016

Post navigation

Previous Previous post: Xambo et al. – 2016 – Learning to code through MIR
Proudly powered by WordPress